In [12]:
!pip install seaborn
!pip install plotly
!pip install numpy

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[notice] A new release of pip is available: 24.0 -> 25.2
[notice] To update, run: python.exe -m pip install --upgrade pip

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[notice] A new release of pip is available: 24.0 -> 25.2
[notice] To update, run: python.exe -m pip install --upgrade pip

Requirement already satisfied: numpy in c:\users\abane\music\da_prject internship\env\lib\site-packages (2.3.3)

[notice] A new release of pip is available: 24.0 -> 25.2
[notice] To update, run: python.exe -m pip install --upgrade pip
In [9]:
!pip install matplotlib

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Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib
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[notice] A new release of pip is available: 24.0 -> 25.2
[notice] To update, run: python.exe -m pip install --upgrade pip
In [1]:
!pip install pandas

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Installing collected packages: pytz, tzdata, numpy, pandas
Successfully installed numpy-2.3.3 pandas-2.3.3 pytz-2025.2 tzdata-2025.2

[notice] A new release of pip is available: 24.0 -> 25.2
[notice] To update, run: python.exe -m pip install --upgrade pip
In [3]:
!pip install openpyxl

Collecting openpyxl
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Successfully installed et-xmlfile-2.0.0 openpyxl-3.1.5

[notice] A new release of pip is available: 24.0 -> 25.2
[notice] To update, run: python.exe -m pip install --upgrade pip
In [4]:
import pandas as pd

df = pd.read_excel('clinical_trial_patient_dataset.xlsx')
df = df.drop_duplicates()
df = df.replace(['NA', 'N/A', 'None', '', 'not mentioned', 'not available', 'unknown', 'absent'], pd.NA)
df = df.dropna()  # or df = df.fillna(method='ffill') for filling missing values
df.columns = [col.lower().strip() for col in df.columns]
In [6]:
# df.describle()
df.describe()
Out[6]:
age bmi systolic_bp diastolic_bp hemoglobin_g_dl creatinine_mg_dl glucose_mg_dl visit_completion_rate missed_visits medication_adherence data_quality_score
count 4500.000000 4500.000000 4500.000000 4500.000000 4500.000000 4500.000000 4500.000000 4500.000000 4500.000000 4500.000000 4500.000000
mean 54.276444 25.139889 124.275333 76.570444 12.401356 1.037151 100.129778 0.549178 1.348000 0.588802 79.869889
std 14.632067 7.958235 32.534530 21.703229 4.156810 0.384391 25.037816 0.385300 1.458703 0.408459 11.953774
min 18.000000 0.000000 0.000000 0.000000 0.000000 0.000000 9.000000 0.000000 0.000000 0.000000 19.300000
25% 44.000000 21.500000 114.000000 68.000000 11.700000 0.850000 83.000000 0.000000 0.000000 0.000000 72.500000
50% 54.000000 25.700000 129.000000 79.000000 13.200000 1.080000 101.000000 0.740000 1.000000 0.810000 81.900000
75% 64.000000 29.900000 143.000000 90.000000 14.700000 1.290000 117.000000 0.860000 2.000000 0.910000 89.100000
max 85.000000 54.700000 199.000000 137.000000 21.900000 2.210000 198.000000 1.000000 9.000000 1.000000 99.900000
In [7]:
df.to_csv('cleaned_data_from_clinical_set.csv', index=False)
In [13]:
import matplotlib.pyplot as plt

stages = ['Screened', 'Enrolled', 'Randomized']
counts = [1000, 700, 600]  # replace with your data

plt.figure(figsize=(8,5))
plt.bar(stages, counts, color='skyblue')
plt.title('Recruitment Funnel')
plt.xlabel('Stages')
plt.ylabel('Number of Patients')
plt.show()
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In [16]:
# import seaborn as sns
# import pandas as pd

# # df with 'enrollment_date' column
# # df['enrollment_date'] = pd.to_datetime(df['enrollment_date'])
# # df_enroll = df.groupby('enrollment_date').size().reset_index(name='count')
# df['enrollment_date'] = df['enrollment_date'].replace('Not Mentioned ', pd.NaT)
# df['enrollment_date'] = pd.to_datetime(df['enrollment_date'], errors='coerce')


# plt.figure(figsize=(12,6))
# sns.lineplot(data=df_enroll, x='enrollment_date', y='count')
# plt.title('Enrollment Trend Over Time')
# plt.xlabel('Date')
# plt.ylabel('Enrollments')
# plt.show()


import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

# Replace invalid dates and convert to datetime
df['enrollment_date'] = df['enrollment_date'].replace('Not Mentioned ', pd.NaT)
df['enrollment_date'] = pd.to_datetime(df['enrollment_date'], errors='coerce')

# Create df_enroll by grouping by enrollment_date and counting
df_enroll = df.groupby('enrollment_date').size().reset_index(name='count')

plt.figure(figsize=(12,6))
sns.lineplot(data=df_enroll, x='enrollment_date', y='count')
plt.title('Enrollment Trend Over Time')
plt.xlabel('Date')
plt.ylabel('Enrollments')
plt.show()
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In [17]:
sites = ['Site A', 'Site B', 'Site C']  # replace with your site names
enrollments = [200, 150, 100]  # replace with your data

plt.figure(figsize=(10,5))
plt.barh(sites, enrollments, color='lightgreen')
plt.title('Site Performance Leaderboard')
plt.xlabel('Number of Enrollments')
plt.ylabel('Sites')
plt.show()
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In [28]:
# # df_pivot: pivot table with patients as rows, visits as columns, adherence as values
# import numpy as np

# df_pivot = df.pivot('patient_id', 'visit_number', 'adherence_flag')
# plt.figure(figsize=(12,8))
# sns.heatmap(df_pivot.fillna(0), cmap='YlGnBu', cbar=True)
# plt.title('Patient Adherence Heatmap')
# plt.xlabel('Visit Number')
# plt.ylabel('Patient ID')
# plt.show()

# import numpy as np
# import matplotlib.pyplot as plt
# import seaborn as sns

# df_pivot = df.pivot(index='patient_id', columns='visit_number', values='adherence_flag')
# plt.figure(figsize=(12,8))
# sns.heatmap(df_pivot.fillna(0), cmap='YlGnBu', cbar=True)
# plt.title('Patient Adherence Heatmap')
# plt.xlabel('Visit Number')
# plt.ylabel('Patient ID')
# plt.show()


# print(df.columns)

# import pandas as pd
# import matplotlib.pyplot as plt
# import seaborn as sns

# # Load dataset
# # df = pd.read_csv('cleaned_data_from_clinical_set.csv')
# df_pivot = df.pivot(index='patient_id', columns='visit_number', values='adherence_flag')

# # Aggregate mean visit completion rate by site and gender
# df_grouped = df.groupby(['site_id', 'gender'])['visit_completion_rate'].mean().unstack()
# # df_pivot = df.pivot(index='patient_id', columns='visit_number', values='adherence_flag')


# plt.figure(figsize=(12,7))
# sns.heatmap(df_grouped, annot=True, fmt=".2f", cmap='YlGnBu', cbar_kws={'label': 'Avg Visit Completion Rate'})
# plt.title('Average Visit Completion Rate by Site and Gender')
# plt.xlabel('Gender')
# plt.ylabel('Site ID')
# plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load dataset
df = pd.read_csv('cleaned_data_from_clinical_set.csv')

# Aggregate mean visit completion rate by site and gender
df_grouped = df.groupby(['site_id', 'gender'])['visit_completion_rate'].mean().unstack()

plt.figure(figsize=(12,7))
sns.heatmap(df_grouped, annot=True, fmt=".2f", cmap='YlGnBu', cbar_kws={'label': 'Avg Visit Completion Rate'})
plt.title('Average Visit Completion Rate by Site and Gender')
plt.xlabel('Gender')
plt.ylabel('Site ID')
plt.show()
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In [39]:
# import plotly.express as px

# dropout_counts = df['dropout_reason'].value_counts().reset_index()
# dropout_counts.columns = ['reason', 'count']

# fig = px.pie(dropout_counts, values='count', names='reason', title='Dropout Reasons')
# fig.show()


# import pandas as pd
# import plotly.express as px

# # Load the dataset
# df = pd.read_csv('cleaned_data_from_clinical_set.csv')

# # Calculate dropout reason counts
# dropout_counts = df['dropout_reason'].value_counts().reset_index()
# dropout_counts.columns = ['reason', 'count']

# # Create pie chart
# fig = px.pie(dropout_counts, values='count', names='reason', title='Dropout Reasons')
# fig.show()



import pandas as pd
import plotly.express as px

df = pd.read_csv('cleaned_data_from_clinical_set.csv')

# Filter out 'Not Mentioned'
filtered_df = df[df['dropout_reason'] != 'Not Mentioned']

dropout_counts = filtered_df['dropout_reason'].value_counts().reset_index()
dropout_counts.columns = ['reason', 'count']

fig = px.pie(dropout_counts, values='count', names='reason', title='Dropout Reasons (Excluding Not Mentioned)')
fig.show(renderer="notebook")
In [41]:
import pandas as pd
import plotly.express as px

# Load dataset
df = pd.read_csv('cleaned_data_from_clinical_set.csv')

# Create and show age distribution histogram
fig = px.histogram(df, x='age', nbins=30, title='Age Distribution')
fig.show(renderer="notebook")